IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024 4429

How Does Automation Shape the Process

of Narrative Visualization: A Survey of Tools

Qing Chen , Shixiong Cao , Jiazhe Wang ,andNanCao , Member, IEEE

Abstract—In recent years, narrative visualization has gained

much attention. Researchers have proposed different design spaces

for various narrative visualization genres and scenarios to facilitate

the creation process. As users’ needs grow and automation tech-

nologies advance, increasingly more tools have been designed and

developed. In this study, we summarized six genres of narrative vi-

sualization (annotated charts, infographics, timelines & storylines,

data comics, scrollytelling & slideshow, and data videos) based on

previous research and four types of tools (design spaces, authoring

tools, ML/AI-supported tools and ML/AI-generator tools) based

on the intelligence and automation level of the tools. We surveyed

105 papers and tools to study how automation can progressively

engage in visualization design and narrative processes to help users

easily create narrative visualizations. This research aims to provide

an overview of current research and development in the automa-

tion involvement of narrative visualization tools. We discuss key

research problems in each category and suggest new opportunities

to encourage further research in the related domain.

Index Terms—Authoring tools, automatic visualization, data

visualization, design space, narrative visualization, survey.

I. INTRODUCTION

ATA visualization has been broadly applied to communi-

cate data and information in an effective and expressive

manner. Recently, an emerging trend has been to combine nar-

rative and storytelling with visualization [1]. The norms of com-

municative and exploratory information visualization are used

in narrative visualizations in order to tell the desired story [2].

However, creating visualizations with narrative information is a

challenging and time-consuming task. Such a creation usually

requires data analytic skills and visualization design expertise.

Even experts need to spend a considerable amount of time and

effort to create the ideal visualization. Therefore, by summariz-

ing the experience in practice, researchers specify various design

spaces and visualization scenarios for distinct narrative genres,

which are used to guide users to create narrative visualizations.

Manuscript received 23 June 2022; revised 7 March 2023; accepted 20 March

2023. Date of publication 27 March 2023; date of current version 1 July 2024.

This work was supported in part by the NSFC under Grants 62002267, 62072338,

and 62061136003, in part by NSF Shanghai under Grant 23ZR1464700, and in

part by Shanghai Education Development Foundation “Chen-Guang Project”

under Grant 21CGA75. Recommended for acceptance by S. Bruckner. (Corre-

sponding author: Nan Cao.)

Qing Chen, Shixiong Cao, and Nan Cao are with the Intelligent Big

Data Visualization Lab, Tongji University, Shanghai 200070, China (e-mail:

qingchen@tongji.edu.cn; caoshixiong@tongji.edu.cn; nan.cao@tongji.edu.cn).

Jiazhe Wang is with the Ant Group, Shanghai Hangzhou 310000, China

(e-mail: jiazhe.wjz@antgroup.com).

Digital Object Identiﬁer 10.1109/TVCG.2023.3261320

With the emergence of new user requirements and the ad-

vancement of automation technology, an increasing number of

intelligent tools have been created to assist the visual creative

process. Authoring tools offer rich interactions that allow users

to adequately control the creation process. However, such tools

still require users to decide on each visualization element man-

ually. To further weaken the barriers and reduce the burdens

of creation, researchers have developed ML/AI-supported tools

and ML/AI-generator tools to support a more automatic process.

ML/AI-supported tools usually provide recommendations as

part of the narrative visualization creation process. Normally,

users need to make their own design choices to achieve the design

outcome. However, ML/AI-generator tools do not require user

expertise in visualization and can generate a complete set of

visualization designs without user intervention.

Recent surveys on automated techniques have focused on

traditional statistical charts [3], [4], [5]. Automatic tools that

support various genres of narrative visualizations have not been

sufﬁciently investigated. However, systematic reviews on how

(and to what extent) automation shapes visual design and visual

narrative processes are generally lacking. The narrative process

describes the primary responsibilities and actions of data visual-

ization storytellers and the types of artifacts that come from these

activities [6]. In addition, most previous studies aim at the cre-

ation process from the visual design level. Advances in artiﬁcial

intelligence and human-computer interaction have brought more

opportunities and challenges to this ﬁeld. Therefore, a state-of-

the-art survey is required to provide a better understanding of

automation involvement in narrative visualization creation tools.

To ﬁll this gap, we collected 91 design spaces and tools

covering the six genres of narrative visualization and classiﬁed

them into four automation levels, allowing us to describe how

automatic techniques could be progressively used in visualiza-

tion design and visual narrative, further allowing users to create

data visualizations. By analyzing the tools of each narrative

visualization genre, we compared the focus of the four levels of

tools in each narrative genre in order for users to easily choose the

appropriate tool to create according to different scenarios. Fur-

thermore, we identiﬁed both mature and less-explored research

directions for automated visual narrative tools and presented

new research problems and future work to assist researchers

in advancing their grasp of the subject matter and pursuing

their investigations. In addition to the state-of-art survey, we

developed an interactive browser to facilitate the exploration

and presentation of the collected design spaces and tools at

http://autovis.idvxlab.com/.

https://creativecommons.org/licenses/by/4.0/

4430 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

II. RELATED SURVEY AND TAXONOMY

In this section, we ﬁrst perform a literature review on narrative

visualization. Then, we introduce papers that are most relevant to

our work. Then, we present our survey scope and methodology.

Finally, we describe the taxonomy of this survey.

A. Narrative Visualization and Storytelling Process

Our research is inﬂuenced by the emergence of narrative

visualization theories and visual storytelling technologies. Ac-

cording to Segel et al. [98] , narrative visualization comprises

three essential components: narrative genres, narrative struc-

ture (methods for organizing plot or information), and visual

narrative (visual methods for generating story experiences and

transmitting messages).

Hullman et al. [74] summarized how automated sequencing

can assist users in making organized choices when creating

narrative visuals. According to Lee et al. [6], aiming to achieve

the goal of message delivery, visual data stories must have a

collection of narrative segments backed by data and presented

in a coherent order. In addition, the process of creating narrative

visualizations is not always linear. This approach can be roughly

categorized into three steps: investigating the data, making a

story, and telling the story. Tong et al. [99] surveyed the literature

on storytelling in visualization, covering the logical concepts of

who is the subject of the narrative visual (creation tool and audi-

ence), how the story is told (narration and transition), and why

we can use visual narratives (memory and interpretation). The

goal for this research is to provide researchers and practitioners

with an overview of the development and research for various

narrative visualization tools.

B. Related Survey

This section outlines the surveys related to automated vi-

sualization techniques and tools. Wang et al. [3] surveyed 88

papers on ML4VIS and explained seven main processes of

machine learning techniques applied to visualization: Data Pro-

cessing4VIS, Data-VIS Mapping, Insight Communication, Style

Imitation, VIS Interaction, VIS Reading, and User Proﬁling.

Wu et al. [4] reviewed recent advances in artiﬁcial intelligence

techniques applied to visual data, examining a number of key

research questions related to the development and management

of visual data and the support provided by artiﬁcial intelligence

for these operations. The study by Zhu et al. [5] is the most

relevant to us, in which they investigated automated visualization

techniques for infographics. However, no previous work has

thoroughly analyzed different levels of automation and how

those tools help the design and creation process of visual sto-

rytelling in different narrative forms. Our effort seeks to give

an overview of available design tools that may assist a variety

of users in various design situations. Moreover, through the

analysis, we identify directions that remain undeveloped for

future research.

C. Survey Scope and Methodology

Our research focuses on narrative visualization tools. Tong

et al. [99] emphasized in their research that narrative visualiza-

tion focuses more on information visualization than scientiﬁc

visualization. In addition, studies on narrative scientiﬁc visual-

ization have been limited; therefore, scientiﬁc visualization was

excluded from our study.

To create the corpus of articles, we gathered from visualization

journals and conferences by using reference-driven and search-

driven methods. We started with a collection of references on

the categorization of narrative visualization in this area for the

selection of reference-driven, and we then broadened the focus

by looking up both citing and cited publications. We completed

two rounds of article gathering for the search-based choices. A

preliminary search for narrative visualizations, relevant design

tools, and best practices was conducted in the ﬁrst round by

using high-impact visualization conferences and publications.

In particular, we selected ﬁve conferences (ACM CHI, IEEE

InfoVis, IEEE VAST, IEEE PaciﬁcVis and IV) and ﬁve journals

(IEEE TVCG, IEEE CGA, and ACM Transactions on Graphics,

Computer Graphics Forum, Visual Informatics). We gathered

a variety of publications by using two search terms (“visual-

ization” and “design space/design guide,” “visualization” and

“authoring tool”) and then evaluated abstracts and full texts to

narrow down our sample.

After this round of article selection, 348 papers and tools were

obtained. To achieve a more precise review of the literature about

narrative visualization, we used narrative visualization genres

and tools (e.g., “data comics” and “design space/authoring

tool,” “infographics” and “design space/authoring tools,” etc.) to

categorize t he papers. Furthermore, we removed programming

tools and domain-speciﬁc application tools, as they are beyond

the scope of this research. Finally, 91 narrative visualization

papers and tools are summarized in Table I and Fig. 1.In

Table I and Fig. 1, we excluded 14 commercial software mainly

because most of them do not have a deﬁnite publication date,

and commercial software tends to have frequent updates and

additional features, which makes it difﬁcult to ﬁx a speciﬁc year.

D. Taxonomy

In this section, we will ﬁrst describe the four levels of automa-

tion and then introduce the detailed classiﬁcation of narrative

visualization in our survey.

1) Tool Classiﬁcation Method: In this section, we categorize

the visualization tools into four groups based on their automation

and intelligence[5], [37].

Design space is a conceptual set of possibilities rather

than a software tool [100]. Design space stresses the

ability to choose from a variety of possibilities and

investigate alternatives [101], [102]. The design space is a de-

scription of all potential design options throughout the design

process. Utilizing basic design principles from current visual-

ization techniques is the most preferred method for building a

design space [103]. Moreover, visual design spaces allow us to

capture some implicit knowledge of graphic designers [104].

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4431

TABLE I

HE DESIGN SPACES AND TOOLS OF MAJOR NARRATIVE VISUALIZATION GENRES

Fig. 1. Number of relevant research publications or tools in different genres

for narrative visualization in chronological order.

We believe that the visualization design space is an attempt to

understand how visualizations are created by designers in a sys-

tematic process, that is, to decompose a design work into several

design elements and arrange them properly. A straightforward

design space makes the design more structured and disciplined,

allowing designers to create designs without relying on pure

feelings. It is also the basis for computers to understand the

design and eventually create tools to facilitate the design process.

Authoring tool encapsulates key software functionalities

and features for content creation [105]. It is an appli-

cation or tool designed for a speciﬁc design purpose.

Authoring tools allow users to create visualizations freely with

interactive features. They usually require designers to design

starting from scratch, allowing designers to have major control

of the creation process. On the users’ side, authoring tools

allow them to understand the creation framework in advance

and eventually interact with the system.

ML/AI-supported tools apply intelligent algorithms to

facilitate visualization creation. Such tools ease visual

generation while ensuring a certain degree of control for

user in the creation process. ML/AI-supported tools focus on

automatically providing some steps or automatically visualizing

some elements, while users need to make decisions on some

important steps to create the visualization. A recommended solu-

tion is usually provided for a particular part of the visualization.

Eventually, users can organize the design content to form the

ﬁnal visualization outcome.

ML/AI-generator tool is even more intelligent, as users

no longer need to participate decision making in the

authoring process. The ML/AI-generator tool is designed

to reduce barriers for amateurs to create visualizations automati-

cally and ease the burdens for experts to search and select without

manually specifying all elements [5]. When the user uploads the

data, this type of tool automates the process and analysis of the

data and can generate a complete set of visual design solutions

without user intervention.

2) Visual Classiﬁcation Method: Segel et al. [98] presented

seven genres of narrative visualization: magazine-style, anno-

tated chart, partitioned poster, ﬂowchart, comic strip, slideshow,

ﬁlm/video/animation. Recently, Roth [75] classiﬁed visual sto-

rytelling into seven genres: static visual stories, long-form in-

fographics, dynamic slides, narrative animations, multimedia

visual experiences, personalized story maps, and compilations

(compilations provide a “visual abstract” that typically links to

further text) [75]. On the basis of their ﬁndings and the pre-

sentation outcomes, we reclassiﬁed the narrative visualization

genres. In this research, magazine style, partitioned posters, and

static visual stories were jointly studied and then collectively

referred to as infographics. Film/video/animation, narrated an-

imations, and multimedia visual experiences are called data

videos. Slideshow, compilations, and long-form infographics are

4432 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

collectively called “scrollytelling & slideshow”. In the literature

review, we found only a few works about ﬂowcharts. However,

many works on timelines & storylines are presented in the form

of ﬂowcharts; thus, we jointly utilized ﬂowcharts and timelines.

Roth et al. [75] found that personalized story maps are similar

to adding annotations to maps; in this study, we classiﬁed them

as annotated charts. In conclusion, we focused on six genres

of narrative visualizations in this survey: annotated charts,

infographics, timelines & storylines, data comics, scrollytelling

& slideshow, and data videos.

We surveyed the literature [98], [99] to further summarize

various tools with different narrative orderings and interactiv-

ity. Segel et al. [98] summarized three kinds of ordering for

narrative visualization: linear (the author speciﬁes this path),

random access (no path is s peciﬁed), and user-directed (users

may choose a route from various available pathways or design

their own). Tong et al. [99] added another ordering type called

parallel (multiple paths can be displayed simultaneously). Apart

from the 38 references listed in the Design Space category in

Table I, some studies proposed techniques or algorithms without

developing a fully functional visualization tool with appropriate

interfaces. Therefore, we selected the 36 visualization tools

that include interactive functions and support the creation of

narrative structures for each narrative genre. Their narrative

orderings and interactivity are also marked in Fig. 2.

As shown in Fig. 2, most tools support linear ordering, and

relatively few support random ordering. On average, the tools

for annotated charts support the fewest narrative ordering types,

while the tools for timelines and data videos support the most

narrative ordering types. Segel et al. [98] proposed six types of

interactions for narrative structures, of which hover highlighting,

ﬁltering/selection/search, and navigation buttons are the three

most common interaction types. As we explored the selected

tools, we discovered two standard interaction types: scrolling

which includes landscape and portrait scrolling, and drawing,

which supports “touch+pen” interaction.

III. A

NNOTATED CHART

Annotated charts use graphics (arrows or trend lines) or

text (data values or commentary) to supplement infor-

mation, adding contextual information to a visualization

to supplement or introduce the meaning of the data. Annota-

tions allow audiences to focus on speciﬁc content or critical

information while retaining complete details of the contextual

data [106], [107].

Design space: Adding annotations to visualizations makes

the detailed information more accessible to users and improves

the memorability of the images [8]. Borkin et al. [8] applied

eye movement studies and cognitive experimental techniques

to verify that adding captions and annotated text to charts

can communicate visual information more effectively. When

annotating charts, both the form of the annotation (text, shapes,

highlights, and pictures) and the kind of desired annotation

(data items, coordinate spaces, structural chart components, and

previous annotations) must be considered [10]. In addition, Kong

et al. [9] deﬁned annotation as a visual cue. They divided the

Fig. 2. A summary of interactive tools in each narrative genre, with the

supported narrative orderings and interactions of each tool.

annotations into two categories: internal cues that modify the

existing image by highlighting or downplaying the focus area

(i.e., the context) and external cues that add supplementary

elements (e.g., outlines, annotations and glyphs) to the existing

image to emphasize the focus. They showed that internal cues

are often more effective in directing attention than external cues.

Internal cues affect the current picture by highlighting the focal

region or de-emphasizing the rest of the visualization.

Authoring tool: Researchers have developed a range of vi-

sual programming libraries and packages for diagram annota-

tions [108], [109]. These tools require users to have program-

ming skills, while programming tools can only provide asyn-

chronous feedback to designers. To help create chart annotation

more easily, researchers have developed authoring tools that

have appropriate interfaces and can provide feedback to users,

which signiﬁcantly facilitates the annotating process without

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4433

Fig. 3. Selected examples of annotated charts’ design spaces and tools. (1) Design space: an experiment on visualizations’ recognition and recall. The study

shows that adding captions and annotating text improves user attention and recall [8]. (2) Authoring tool: ChartAccent [10], which allows one to manually and

interactively generate data annotations. (3) ML/AI-supported tool: Kori’s [15] Tools viewport. As the user enters text, Kori automatically prompts for potential

references (gray underlining). Simple interactions to manually create links are also supported. (4) ML/AI-generator tool: annotations generated by Contextiﬁer

[20].

requiring specialized programming knowledge. Tableau [110]

provides several basic options for annotating charts. For exam-

ple, the tool allows users to add trend lines to charts. User-created

annotations via text can be data-driven but are limited to some

standard forms of annotation. ChartAccent [10] is an interactive

tool that allows users to generate data annotations manually. It

offers many functions, s uch as highlighting markers, which are

more straightforward and ﬂexible than Tableau [110]. Selected

markers can be highlighted directly without affecting unselected

markers. Although these tools can easily create annotations,

they still rely largely on the designer’s expertise to create

manually.

ML/AI-supported tool: ML/AI-supported tools of annotated

charts reduce manual operations by automatically providing

annotated suggestions via user interactions. SmartCues [19],

which provides multitouch interaction, is a library that supports

details-on-demand via dynamic computational overlays to assist

users in building queries and generating data-aware annotations.

Touch2Annotate [11] and Click2Annotate [12] are early semi-

automatic annotation generators. Touch2Annotate [11] is a tool

for adding annotations to multidimensional data visualizations

on a multitouch interface. The tool provides annotation tem-

plates and allows users to create high-quality chart annotations

by simply highlighting the data and selecting the appropri-

ate annotation template according to the annotated content.

Click2Annotate [12] allows simple data analysis and generates

easy-to-understand annotations. The semantic information en-

coded in its annotations can be browsed and retrieved. Simi-

larly, Kandogan [13] proposed just-in-time descriptive analysis,

where interacting with a diagram automatically annotates it.

Latif et al. [15] developed Kori based on a design space

analysis of textual and graphical references and added visual-

ization genres, such as line charts, pie charts, and maps. When

users create visualizations with the tool, the system automat-

ically provides annotation suggestions using natural language

and enables combining text and graphs via manual interaction.

Kong et al. [17] proposed an automated system that overlays

user-selected graphics onto existing chart bitmaps and allows

users to customize published visualizations by identifying visual

markers and attributes of axes of encoded data to better assist

users with chart reading tasks. Srinivasan et al. [18] explored

the potential applications of interactive data facts for visual data

exploration and communication. The researchers also developed

the Voder system to demonstrate how users can use interactive

data facts to suggest optional visualizations and modiﬁcations,

which helps users interpret the visualizations and convey their

ﬁndings. Bryan et al. [14] focused on narrative visualizations

for multivariate, time-varying datasets. They proposed a method

called Temporal Summary Images (TSI) consisting of temporal

layout, data snapshots in the form of comic strips, and textual

annotations. Moreover, researchers have noted that line graphs

are the most common type of visualization in daily life [111].

4434 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

However, some line charts are deceptive with exaggeration, un-

derstatement, and message reversal. For example, exaggerating

or minimizing the effect size via aspect ratio manipulation in

line charts leads to deceptive representation [16]. To address

this problem, Fan et al. [16] introduced a tool for detecting and

annotating line graphs in the wild that reads line graph images

and outputs text and visual annotations to assess the truthfulness

of line graphs and help readers understand faithful line charts.

Compared with authoring tools, ML/AI-supported tools fur-

ther simplify the difﬁculty of creating annotated visual dia-

grams and reduce manual operations by automatically providing

annotation suggestions. Furthermore, ML/AI-supported tools

allow users to promptly add annotations to the diagram while

interacting with the visualization based on AI assistance.

ML/AI-generator tool: As annotations are essential in visual-

ization design, researchers have explored annotation approaches

for different visualization genres. The Contextiﬁer [20] provides

an algorithm for selecting annotations that automatically creates

a stock timeline graph and matches the appropriate annotation

to the line graph by referring to the content in the news article.

Liu et al. [21] developed AutoCaption to build a scheme to

accomplish the task of diagram title generation by using deep

neural networks. One-dimensional residual neural network is

used to analyze the relationships between visualization ele-

ments, identify essential features of the visualization diagram,

and generate a complete description. Both tools create t he ap-

propriate information for the diagram without user intervention.

Summary: Annotations are informative additions to visual

diagrams and are an essential part of visual design, helping

audiences quickly understand diagram information and help-

ing analysts revisit and reuse analysis processes conducted in

the past [112]. Researchers have veriﬁed the importance of

annotation at the visual memory level [7] and at the cognitive

level [15], which both indicate that annotations are an integral

part of visualization design. Although researchers have studied

the layout problem of annotated charts and the distraction caused

by repeatedly switching views by using interactive highlighting

[113], solutions to occlusion problems, such as annotations

blocking the charts, have not yet been addressed. Therefore,

more advanced techniques and tools are required to improve

the efﬁciency of the automatic layout. Moreover, for tools to

become more intelligent and accurate, the extraction of the

existing annotated diagram corpuses and the research related to

the identiﬁcation and correction of incorrect annotations must

both be enhanced. Researchers have also developed various

tools based on annotated design spaces. Just-in-time annotations

and automated annotations provide a new method for users

to promptly update and convey visual information [106].In

the future, automated annotations can focus more on internal

annotations with the option of rich and aesthetically appealing

visual cues [5].

IV. I

NFOGRAPHIC

The term infographics, which stands for informational

graphics, refers to a type of visualization that focuses on

the use of graphically designed icons, images, colors, and

other elements to illustrate data and textual information. Otten

et al. [114] deﬁned infographics as “to convey a particular set

of information to a speciﬁc audience by transforming complex

and abstract concepts into visual components.”

Design Space: Infographics are frequently utilized in a va-

riety of sectors because they are simple to comprehend and

can improve the viewer’s visual working memory [23], [115].

Different categories of infographics, information units, and pre-

sentation formats have been studied by researchers. Albers et

al. [116] summarized four types of infographics, including

bullet list infographics, snapshot infographics, ﬂat information

infographics, and information ﬂows. Infographics can also be

classiﬁed into static, dynamic, and interactive categories based

on their presentation forms.

A good infographic should be attractive, easy to understand,

and easy to remember [22]. Studies have found that audiences

usually form a primary impression of an infographic within the

ﬁrst 500 milliseconds. This impression depends heavily on the

color and visual complexity of the page. Therefore, to increase

the appeal of infographics, designers should display them by

increasing the contrast between colors or selecting a limited

number of images with text [23]. However, an infographic is not

only a simple combination of graphics and text. Infographics af-

fect how well audiences remember information; when audiences

are pleased by infographics, they are more likely to remember

it over a longer time period [24]. The studies by Lan et al. [25]

showed that adding emotional factors to visual designs can create

better infographics. Other researchers point out that embedding

games into infographics encourages user interaction and im-

proves their exploration experience [26]. In addition, several

speciﬁc design guidelines for infographics are proposed. Dunlap

& Lowenthal [27] gave design recommendations on four levels:

overall design, structure, content, and infographics visuals.

Authoring Tool: Infographics have many advantages, but de-

signing infographics can be laborious for amateurs and time-

consuming even for experts. Numerous tools can be used to

create infographics in the design ﬁeld, including Adobe Il-

lustrator [117],Sketch[118], and other vector drawing tools.

However, these tools do not support associating data with graph-

ics, suggesting the complexity involved in matching data with

graphics when used together to create data-driven infographics.

Researchers have developed specialized tools to solve this prob-

lem by binding data to vector graphics. For example, designers

can manually draw graphics and associate data with the created

graphics by using Data-Driven Guides (DDG) [28]. This tool

relieves the burdens of designers to manually code data into

custom graphics. Chartreuse [29] and InfoNice [30] help users

create evocative bar graphs with custom markers that convert

new bars into infographics with visual elements. Both tools

are integrated with Microsoft Ofﬁce as plug-ins, lowering the

barrier to creating infographics. In addition to associating data

with vector graphics, DataQuilt [31] and Infomages [32] are

tools for binding data to bitmap images. In addition, certain

tools are integrated with the sketch functions, allowing users to

create designs more freely [33], [34], [35]. DataInk [34] provides

“pen+touch” interactions enabling designers to express their

creative thinking by drawing on a digital canvas and directly

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4435

Fig. 4. Selected examples of infographic’s design spaces and tools. (1) Design space: different types of infographics have different levels of appealtousers,the

scores are the means and standard deviations from user experiments using 9-point Likert scale) [23]. (2) Authoring tool: DDG vector drawing tool which can be

used to bind vector graphics to data [28]. (3) ML/AI-supported tool: utilizes a deep neural network using manually labeled infographics as training data to ﬁnd

visual data items while ignoring creative aspects [36]. (4) ML/AI-generator tool: infographics are automatically generated by simulating online examples in two

main steps: retrieval (indexing of online instances based on visual elements) and matching (replacement with personal user data) [40].

matching their drawings to data. SketchStory [35] combines

real-time free-writing with interactive data charts, enabling pre-

senters to move and resize charts by touching the screen. This

feature facilitates the creation of personalized and expressive

data charts. Although all these tools can help create infographics,

most tools can only transform speciﬁc data types into speciﬁc

forms of visual charts, with line charts and bar charts being the

majority. Designers still need to reintegrate the design elements

and lay them out to form complete infographics.

ML/AI-Supported Tool: Lu et al. [36] built an infographic

visual ﬂow search tool, VIF-Explorer, by analyzing many info-

graphics and extracting the Visual Information Flow (VIF) of

these images. However, this software can only analyze simple

infographics. Complex or nonstandard infographics with cre-

ative elements are challenging to identify and characterize. In-

fographics Wizard [37] can generate infographics with complex

layouts. The tool ﬁrst recommends VIF layouts based on the

given information, then provides recommendations for visual

group (VG) designs, and ﬁnally generates connections between

VGs to complete the infographics. Visme [119], Infogram [120]

and Canva [121] are examples of more commercial types of

software. These web-based tools allow users to drag and drop

various images and graphic elements to create infographics of

the highest quality. Additionally, an infographic’s colors have a

signiﬁcant impact on the audience’s ﬁrst impression [23], [115].

InfoColorizer [38] allows users to employ color palettes to create

data-driven infographics.

In short, ML/AI-supported tools for infographics aim to

identify existing i nfographic layouts and color encodings and

match them to corresponding infographic recommendations.

While it could offer more design options and save efforts for

designers, the existing ML/AI-supported tools are not intelligent

enough to make creative and unique infographics similar to those

created by designers who use authoring tools.

ML/AI-Generator Tool: Text-to-Viz [39] generates infograph-

ics by natural language techniques with predeﬁned schemes in

two steps: semantic parsing (identifying how this information

is described by casual users) and visual generation (layout,

descriptions, graphics, and colors). However, the tool is limited

in three aspects: the generability problem, which only supports

proportion facts; infographics expressiveness, which is based

on predesigned styles; and expression ambiguity, which the

current model cannot understand. Qian et al. [40] proposed

Retrieve-Then-Adapt to automatically generate infographics by

simulating Internet design works so that it can create richer

designs. Chen et al. [41] proposed a similar solution in that

it helps users turn existing timeline infographics into re-editable

templates. In the deconstruction phase, a multitask deep neural

network is used to parse the global and local information on

the timeline; in the reconstruction phase, the infographic is

then extended into an editable template by a channel technique.

These approaches identify and visualize accurate information

and ensure that the ﬁnal generated infographic elements are

organized harmoniously.

4436 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

Fig. 5. Selected examples of timeline’s design spaces and tools. (1) Design space: Brehmer et al. [42] proposed that storytelling with a timeline encompasses

three levels of design space: representation, scale, and layout. (2) Authoring tool: Timeline Storyteller’s [49] working viewport, where the timeline canvas spans

the entire browser window. (3) ML/AI-supported tool — Left: the working window of TimeLineCurator [55], a browser-based authoring tool. The diagram depicts

a chronology of Scandinavian pop music, with each hue denoting a different nation. Right: Example of a storyline visualization created using PlotThread [53].The

layouts are developed collaboratively by AI agents and designers, while styles and visual labels are manually modiﬁed to enhance the narrative.

Summary: Different types of tools have different focuses

for infographic design. Design spaces of infographics mainly

introduce the key components of a good infographics. For au-

thoring tools, the focus is on how to bind images with data.

ML/AI-supported tools and ML/AI-generator tools identify the

layout of existing infographics and apply or recommend it to

new infographics. Creating infographics with authoring tools

and ML/AI-supported tools requires users to know what the ﬁnal

infographics look like, which can be challenging for amateurs.

ML/AI-generator tools are more friendly to amateur users. These

tools help users generate visualizations from data insights and

design aesthetics by using an automated approach that reduces

the complexity of the creative process and effectively increases

productivity.

Although a great deal of research has been conducted, much

work is still required in this category. The ﬁrst direction is to

adapt current tools to more visualization genres. Existing tools

for converting standard statistical charts into infographics sup-

port only simple chart conversions [29], [30]. A more compre-

hensive visual corpus needs to be built to support a wider variety

of visualization genres in future work. The second direction is to

offer more advanced extraction and editing functions to existing

infographics. Some tools can identify design elements from

existing infographics, but only support simple visual charts [39],

[40], [41]. Meanwhile, the extraction of artistic effects in info-

graphics is still relatively weak and intelligent algorithms can be

applied to tackle this problem. Moreover, editing functions can

be added to infographic identiﬁcation tools directly to reduce

the effort of switching between software. The third direction is

to enhance research on intelligent algorithms. Many rule-based

algorithms are applied in current tools (e.g., color selection [38]

and icon selection). The quality of infographics generated by

visualization systems can be further improved using more ad-

vanced machine learning or deep learning approaches.

V. T

IMELINE &STOR YLINE

Timeline and Storyline describe sequences of

events [42]. The most typical timeline has events

arranged horizontally according to their timestamps

and a horizontal axis used to represent time progression from

left to right [50]. In a storyline visualization, the narrative

unfolds from left to right; each person is represented as a

line. When two people interact at the exact moment, their two

lines intersect [45], [52]. As their presentations share many

resemblances, timelines and storylines are jointly discussed in

this section.

Design Space: Brehmer et al. [42] proposed that storytelling

with timelines contains three levels of representation (e.g.,

linear, radial, and grid), scale (e.g., relative and logarithmic),

and layout (e.g., uniﬁed and faceted). Moreover, by combining

these three levels, 20 timeline design options were identiﬁed to

match the narrative style. Lan et al. [43] identiﬁed six narrative

sequencing patterns (chronology, trace-back, trailer, recurrence,

halfway-back and anchor). The study results showed that nonlin-

ear narratives are more likely to increase user engagement and

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4437

that nonlinear narratives enable stories to be more expressive

without hindering comprehension. Bach et al. [44] proposed

the concept of time curves for nonlinear narrative visualization.

The aims of their work were to provide a general method for

producing straightforward visual summaries for a variety of

temporal datasets. The researchers describe the visual patterns

that time curves often display (i.e., cluster, transition, cycle,

U-turn, outlier, oscillation, and alternation) and how to interpret

them. Similarly, Kim et al. [48] suggested the use of story curves

to analyze and convey nonlinear narratives in ﬁlm. Story curves

in this style may be used to establish the general ordering of

events by comparing the order of events in a ﬁlm to their actual

chronological order.

However, storyline visualization is usually limited in that par-

ticipants cannot belong to two different groups simultaneously.

As a participant is represented as a line, multiple lines bundled

together at a time point usually indicate that they belong to the

same group at that time. However, when the participant belongs

to different groups simultaneously, for example, in co-author

relationships, the participant’s line of thinking is difﬁcult to align

with that of the co-authors. To solve this problem, Di Giacomo

et al. [45] proposed a model that aims to present participants

with a tree diagram rather than a line diagram. In addition,

several researchers have proposed a series of design guidelines

regarding the timelines’ aesthetics and readability, which can be

roughly divided into three categories: (1) attempt to keep straight

lines to minimize line crossings [46], [47], [52], (2) the same

set of lines should appear next to each other, and (3) a certain

distance should exist between lines [52] . These design guidelines

are proposed to provide a theoretical basis for creating timelines,

which can be used to guide users to better create timelines in

authoring tools.

Authoring Tool: Creating timelines can be a time-consuming

task for novices; consequently, researchers have developed

several authoring tools for creating timelines [122], [123],

[124], [125]. Two of the most commonly used tools are

TimelineJS [124] and TimelineSetter [125]. Both tools can

automatically generate a visual timeline by ﬁlling in dates and

titles, describing events in Google spreadsheets, and linking to

corresponding images, videos, and other media. The generated

timeline can also be demonstrated in the form of slides [124],

[125]. Although these tools are popular, they have limitations.

They cannot generate timelines for nonlinear storylines or com-

plex layouts. Kim et al. [48] developed Story Explorer based

on Genette’s [126] research on story events. This tool enables

users to organize the chronology of scenes in a movie script and

explore nonlinear narratives using story curves.

However, several of the abovementioned tools can only create

linear timelines. Before Timeline Storyteller [49] was devel-

oped, designers who wanted to convey expressive stories by

using special timeline layouts (matrices, spirals, etc.) usually ap-

plied time-consuming manual approaches or programming im-

plementations. However, timelines created in using this method

often lacked guidance in balancing the perception and narra-

tive effects, resulting in being difﬁcult to understand [127].To

solve this problem, Brehmer et al. proposed a timeline design

space [42] and further developed tools [49] that would easily

allow users to create nonlinear forms of timelines.

Although these authoring tools have lowered the threshold for

users to create timelines, several challenges at the layout and

visual encoding level still need to be addressed. For exam-

ple, when designers need to ﬁnish hundreds or thousands of

timelines, it becomes difﬁcult to meet both the aesthetics and

readability principles of the timeline design. It is also time-

consuming and technically difﬁcult for designers to manually

adjust the layout to avoid line crossings and overlaps. ML/AI-

supported tool: Some ML/AI-supported tools in the timeline vi-

sualization domain solve the abovementioned problems. Time-

Sets [50]

uses the “gestalt principles” of proximity and uni-

formity of association to group together the relevant events

and the use of backdrop colors to visually link collections’

activities. The tool addresses the visual inconsistency caused by

too many lines. StoryFlow [51] uses a new hybrid optimization

strategy that combines discrete (sorting and aligning line entities

to create the initial layout) and continuous (optimizing the

layout based on convex quadratic optimization) optimization

methods to quickly create timelines with aesthetic and readable

properties. However, this approach is insufﬁcient in effectively

supporting advanced design preferences, such as changing the

general trend of lines [52]. Tang et al. [52] created iStoryline to

create more meaningful storyline visualizations that satisfy the

needs of designers. This tool integrates user interactions into an

optimization algorithm that allows users to easily create story

visualizations by modifying the automatically generated layouts

according to their preferences.

While iStoryline’s [52] interactions focus on modifying local

areas, customizing the overall layout is time-consuming and the

optimization process is unpredictable, which requires repeated

trials to optimize the results. To improve the user experience,

PlotThread [53] integrates AI agents into the authoring process.

The AI agent can decompose a given storyline into a series

of segments, allowing the user to understand the state of the

intermediate layout and predict the following action. In addi-

tion, Ellipsis [54] and TimelineCurator [55] are both timeline

authoring tools focused on the ﬁeld of journalism. Ellipsis [54]

blends a domain-speciﬁc language for narrative development

with a graphical user interface framework. TimelineCurator [55]

can process unstructured documents with temporal text by using

natural language and subsequently extract the temporal text

from them along the way. These tools signiﬁcantly facilitate the

management and processing of documents containing timelines.

Summary: Timelines and storylines are used to depict event

progressions. Researchers focus on timeline aesthetics and nar-

rative impact in timeline & storyline design. Users can manually

design timelines for particular scenes (i.e., movie narration) or

use authoring tools (i.e., matrices and spirals). ML/AI-supported

tools leverage intelligent algorithms to assist users in creating

narratives by sorting temporal sequences and text information

from unstructured raw data. It also enhances the aesthetics and

usability of timelines and makes writing tools more efﬁcient.

We observed that a signiﬁcant amount of text information can

be easily processed using ML/AI-supported tools, whereas a

4438 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

Fig. 6. Selected examples of data comics’ design spaces and tools. (1) Design space for data comics design patterns and illustrations of some examples

in data comics [60]. (2) Authoring tool: DataToon’s working viewport, which can create dynamic web data cartoons through pen-touch interaction [64].(3)

ML/AI-supported tool: Chartstory’s working viewport, which automates the analysis, layout, and creation of captions for data comics that tell tales using data [68].

(4) ML/AI-generator tool: Calliope [70] automatically generates visual data tales from spreadsheets and includes a story generator and editor.

limited amount of text and a particular type of timeline can be

created using authoring tools.

Following the research directions indicated by the existing

studies, we believe the following directions may be studied in

the future. The ﬁrst direction is to explore the need for special

forms of timelines. Although Brehmer et al. [42], [49] proposed

six forms (i.e., linear, radial, spiral, curved, calendar, and grid)

of timeline representation, their study mainly focused on two

forms, linear and radial. Moreover, the representations of these

particular timelines determined by the researchers have not been

veriﬁed in terms of user acceptance and communication effec-

tiveness. Future work needs to validate these representations via

formal experiments and implement more real-world applications

of such new forms of timelines. The second direction is that the

existing authoring tools often overlap timelines when creating

content with multiple temporal texts, and the subjective merging

of timelines for aesthetic reasons results in the loss of informa-

tion. In the future, we also need to strengthen the research in

this area, ensuring the integrity of information while achieving

the aesthetic goal. In the realm of timelines and storylines,

ML/AI generator tools are still in their developmental stages.

While current ML/AI-supported tools can assist users in creating

timelines, they are primarily utilized for localized adjustments

and fall short in terms of fulﬁlling the demands of the complete

content creation process. The future holds immense potential

for the research and development of advanced ML/AI generator

tools for timelines.

VI. D

ATA COMICS

Data comics are an emerging form of narrative visual-

ization [98] that focuses on the variation of data informa-

tion and the visual presentation of data sequences [58].

Different from traditional comics, data comics must contain

data-driven content, allowing multiple visualizations to be jux-

taposed in a single panel in a comic strip layout, with annotations

and visual decorations [56]. Data comics complement the linear-

ity of narratives that are inherently imposed by movies and live

presentations while offering the ﬂexibility of two-dimensional

spatial arrangements in infographics and annotated charts [60].

Design Space: Comics are a static format that is great for

ideation and storyboards [57]. Given that the technical barri-

ers are low, comic creation can be shared and distributed in

various formats, such as scientiﬁc papers, conference posters,

slideshows, blogs, etc. The sequential nature of data comics

and the tight integration of text and graphical information have

great potential to explain complex data and to promote visual-

ization and data literacy [59]. Data comics have the potential to

transform the manner we envision and produce infographics and

presentations because they can convert storytelling approaches

from one medium to another [60]. Furthermore, data comics

are i ncredibly ﬂexible and communicative. They can be used to

integrate graphic elements of comic properties with textual ex-

planations and deliver visual content that requires memorization

and quick navigation [61], [62].

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4439

Although data comics have many advantages, creating good

data comics is a complex task. Designers must consider many

tradeoffs, such as balancing repetition and highlighting, and the

results rely signiﬁcantly on the expertise of designers. Zhao

et al. [62] addressed the issue of data comics view ordering by

examining the narrative mechanism of comic strips. The order

of the data comic panels must be shown to help recall details in

data comics. Wang et al. [61] conducted a user study to compare

data comics and infographics in terms of the degree of clarity of

reading order and the degree of integration of text and images.

The ﬁndings demonstrated that complicated spatiotemporal data

are difﬁcult to depict using infographics, while it is possible to

present with data comics. The participants enjoyed reading data

comics in the experiment and regarded them as more entertaining

and more effective at retaining their attention.

In another study, Zhao et al. [56] compared data comics with

PowerPoint [128]. The results also showed that data comics

are more attractive, more space-efﬁcient, and more enjoyable

to use than PowerPoint [128]. Moreover, as the narrative style

of comics is usually linear, a possible approach is to transform

data comics into data videos with appropriate tools. Mean-

while, comics can present speciﬁc moments in separate frames,

allowing for a more focused presentation of individual data

information [62].

To help people comprehend the art of data, visualization,

narrative, and the necessity for efﬁcient data-based communica-

tion, Bach et al. [60] offered a collection of data comic design

patterns. They also constructed six design patterns for data

comics according to different associations and layout methods.

Some researchers further validated the usefulness of this design

space in practical cases. For example, Hasan et al. [63] created

an interactive data comic in the form of a card game. Each comic

panel becomes an individual card instead of being arranged

in a ﬁxed sequence; learners can form different storylines by

combining them in different ways. Their research showed that

transforming data comics into card games allows learners to

grasp information quickly via interaction and encourages col-

laborative thinking among participants.

Authoring Tool: Researchers have developed various tools

to create data comics to enhance the potential user experience.

DataToon [64] is a tool for creating dynamic web data comics

that support “pen+touch” interactions. The tool allows quick ex-

ploration of data, rapid generation of visual stories with custom

annotations, and interactive ﬁltering of layout templates. How-

ever, displaying exploration data and presentation information

on the same page can cause visual distractions. Kang et al. [65]

solved this problem by proposing ToonNote. ToonNote provides

two view modes: notebook view, which adopts the format of a

traditional computing notebook to conduct data analysis, and

comic layout, which focuses on visual storytelling.

Suh et al. [67] developed CodeToon, a tool that supports the

comic creation process by adopting two mechanisms. One is to

facilitate the conception of code-related stories via metaphorical

recommendations; the other is to generate comics from stories

automatically. Both mechanisms allow users to add codes or

select code examples provided by the tool, generate a story, and

automatically produce comics. The tool allows users to quickly

and easily create high-quality coding strips. To enhance the

user experience of data comics, Wang et al. [66] proposed a

lightweight declarative scripting language, Comic Script, which

supports adding interactivity to static comics. Their work over-

came limitations of the original narrative mode, which can only

produce linear or unchangeable stories. They achieved nonlinear

narratives, personalized layouts, and explored potential user

experiences and detail levels.

ML/AI-Supported Tool: ChartStory [68] is a tool that automat-

ically converts a collection of charts into a data comic format.

It divides charts into clusters of story segments by identifying

narrative segments and then reorganizing the segments to gen-

erate a story. Users can further reﬁne the generated data comics

via interaction.

ML/AI-Generator Tool: Fact sheets present multiple data facts

via visualization in a juxtaposed format that is highly similar

to data comics. In a fact sheet, a data story is constructed

from several facts and numerical or statistical ﬁndings produced

from data [69]. Although some comic elements are missing in

fact sheets, we still categorize them in this category because

they can be easily extended to data comics by adding some

comic-style decorations. Both DataShot [69] and Calliope [70]

can automatically generate fact sheets. DataShot [69] transforms

tabular data into fact sheets by adopting a three-step process

of fact extraction, fact combination, and visual synthesis. This

tool can effectively reduce the difﬁculty of data exploration,

create information presentations and enhance the readability of

data by means of expressive visual design. Calliope [70] extends

this method by automatically creating visual data stories from

spreadsheets using the Monte-Carlo tree search technique to

explore story fragments and arrange them logically. Calliope

generates coherent stories with consistent logical connections

between segments, thereby lowering the threshold for creating

data stories.

Summary: Although in its infancy now, data comics have

gained much attention in recent years. According to some

preliminary studies [56], data comics perform better than

slideshows and infographics in terms of spatial efﬁciency and

reader enjoyment. However, a more detailed evaluation with

a larger number of participants needs to be conducted to val-

idate its usage and effectiveness in practice. Moreover, while

data comics possess a leisurely and entertaining nature, they

are occasionally applied in serious and sensitive contexts. For

example, Charité in Berlin regularly uses comics to educate heart

surgery patients, demonstrating t he practical applicability of this

medium outside of research settings [129].

Almost all the existing tools for creating data comics support

basic data exploration and analysis. While authoring tools can

reduce the difﬁculty of creating data comics, they are targeted at

users who have a certain level of visualization creation skills,

which is not user-friendly to amateurs who want to create

data comics from scratch. ML/AI-supported tools and ML/AI-

generator tools for creating data comics integrate the ability to

analyze data, visualize the analyzed content, and present the

information in a narrative format. The difference between the

two types of tools is that ML/AI-generator tools can automati-

cally analyze data and arrange the data insights into comic-style

4440 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

narratives directly. By contrast, ML/AI-supported tools require

users to select valuable insights or manually layout the panels of

data comics. Reﬂecting on the collected work presented above,

we think that the data comics can be studied in the following

aspects in the future. First, the forms of comics vary to a large

degree, and current research has ignored how different data

types are suitable for which kind of design style and narrative

strategy [59] and which style of data comics users prefer under

what circumstances. Second, the redundant and non-data related

visual elements in data comics can sometimes be confusing and

distracting to viewers, imploring the necessity to explore how

the number of comics grids, the amount of text, the layout, and

the color scheme can be designed to be more acceptable by users.

Third, incorporating interactive features into data comics could

enhance user understanding and engagement [62], despite their

being a static medium.

VII. S

CROLLYTELLING &SLIDESHOW

The term “scrollytelling” is a combination of “story-

telling” and “scrolling.” It is a scrolling-based visual

narrative form that is widely used in data-driven arti-

cles [71]. Scrollytelling articles usually start with a full-screen

photo or video and scroll by considering the next part of the

content [71]. A similar form of visual narrative to scrollytelling is

the slideshow [75], [130]. Mckenna et al. [131] noted that many

recent websites integrate buttons and sliders, demonstrating that

the distinction between the stepper and the scroller depends on

whether the user input is clicking the stepper or scrolling the

slider. In addition, in terms of story layout, pages often appear as

slideshows or hybridsthat combine features of both slideshows

and steppers, with different animations and scrolling. They

resemble both steppers and scrollers, in which the latter form

supports scrollytelling. As the slideshow form and the hybrid

form can be interconverted, we jointly studied scrollytelling and

slideshow.

Design Space: Scrollytelling articles are usually text-centered

and use multimedia elements s uch as images and videos to assist

narrative storytelling [72]. Various transition styles between

pages can be triggered by scrolling. The choice of transition

styles is usually determined by the relationship between facts

(e.g., comparative, similar, and sequential). Scrollytelling can be

used as visual cues, such as highlighting facts in visualization,

to direct attention or to indicate stages to assist browsing [79].

A slideshow is composed of a collection of slides instead of

continuous content in scrollytelling. Elias et al. [73] reviewed the

elements that comprise a slideshow presentation, identifying six

typical elements: slide title, text box, image, embedded content,

equations, and tables to ensure accessibility. Hullman et al. [74]

analyzed 42 narrative visualizations in the form of slides and in-

vestigated how the choice of order affects narrative visualization.

For slideshows, the narrative is told by discrete clicking, tapping,

keying, or swiping dynamic slideshows, allowing the designer

to control the storytelling pace. In addition, users can add or

remove pages to the slideshows according to their needs and can

exit the presentation page at any time. Slide layouts can show

continuous progress between slides or support nonlinear breaks

in the narrative [75]. However, when readers have to navigate

too many pages, they may eventually suffer from boredom, but

too few pages also hinder the user from remembering the story.

Therefore, the story’s length in the slides must be accurately

established [79].

Authoring Tool: Scrollytelling is a challenging task. Idyll [76]

provides a “scroller” component for building scrolling narra-

tives, allowing users to control document style, layout, and

control pages by clicking or scrolling. Sultanum et al. [77]

explored a data-driven approach to article story creation that

separates semantic, textual, and graphical links and story layout

forms. On this basis, researchers developed VizFlow [77],a

tool for creating dynamic data-driven articles. With a text-chart

linking strategy, VizFlow allows users to create dynamic layouts

for static data-driven articles.

Users have more options or tools to create slideshows com-

pared with scrollytelling. The most popular ones are Power-

Point [128], Keynote [132] and Google Slides [133]. This type

of software aims at helping users manually create a set of

slideshows that contain text, images, and other multimedia con-

tent. Providing abundant design templates allows users to focus

on the information they want to present rather than spending

plenty of time in the visual layout [134]. ML/AI-supported tool:

Users often employ slideshows for presentations or speeches.

However, it usually takes considerable time and effort to create

slideshows before the presentation, and for impromptu speech,

users cannot create slideshows in such a short time. Tedric [78]

is a tool to construct a coherent slideshow from a single subject

idea. This tool blends a semantic word web with text and picture

data sources to produce a slideshow that matches the subject.

The user studies conducted by the authors demonstrated that the

use of the tool signiﬁcantly reduces the barriers to impromptu

speech and saves users much time.

ML/AI-Generator Tool: Leake et al. [135] developed a system

that converts text into speech by recognizing speciﬁc words

in each sentence and automatically selects relevant images to

transform these texts into audiovisual slides. Lu et al.

[79]

proposed a method for automatically generating scrollytelling

visualizations. The method begins by listing the data facts for a

given dataset, scores the facts and arranges them into stories, and

then produces visualizations, transitions, and text descriptions

for the scrolling display. However, since existing work in this

category is mostly prototypes, practical use of ML/AI-generator

tools for scrollytelling remains unproven.

Summary: Scrollytelling is a scrolling view of content, an

interaction that is consistent with our everyday behavior of

browsing web pages and articles on mobile devices. A slideshow

is another common display that is a step-based display. Although

we often encounter the two forms of narratives in daily practice,

academic research on slideshow and scrollytelling is generally

lacking. First, as mentioned in the timeline chapter, nonlinear

narratives are more likely to engage users, and scrollytelling and

slideshows can use both linear and nonlinear ways of presenting

information. Scrollytelling and slideshows allow the audiences

to explore different paths by referring to the content based on

their own interests and needs. Instead of following a predeter-

mined linear sequence, the audience can select their own journey

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4441

Fig. 7. Selected examples of scrollytelling design spaces and tools. (1) Design space: Outlines how to use automatic sequencing in design systems to guide

non-designers in making structured decisions when creating narrative visualizations [74]. (2) Authoring tool: uses text-chart links to transform static data-driven

articles containing text and charts into dynamic content [77]. (3) ML/AI-supported tool: Tedric system workﬂow, which can be used to train presentation skills,

reduce barriers to impromptu speaking and generate slideshow based on audience suggestions [78]. (4) ML/AI-generator tool: A method for automatically generating

scrollytelling v isualizations [79].

by referring to the information by clicking on links, making

selections, or following different branches of the narrative. This

approach gives the audience more control over the pace and order

of information, allowing them to focus on the aspects that are

most relevant or meaningful to them. Future work can investigate

whether other nonlinear narrative structures are also suitable

for scrollytelling or slideshow. Second, existing research has

focused on different media combinations, such as images, text,

and video, with minimal research on data visualization and

intelligent tools. In particular, slideshow creation tools are in-

explicitly designed to create narrative visualizations. Therefore,

future research can investigate needs and design requirements

for narrative visualization, thus providing more support to create

data-driven scrollytelling and slideshows.

VIII. D

ATA VIDEO

Data video is a narrative visualization type [98] that

combines data visualization with motion graphics and

tells data-driven stories. Data videos can present viewers

with diverse visual information in a short period, and therefore,

they are widely used in disseminating data information [80],

[90]. Design space: Researchers have primarily focused on

understanding, creating, and disseminating data videos. Amini

et al. [80] ﬁrst proposed a visual narrative structure theory,

in which the narrative structure of data video can be divided

into four roles: establisher (E), initial (I), peak (P), and release

(R). On this basis, Cao et al. [81] presented a more extensive

taxonomy of data video, including four narrative structures, ﬁve

main genres, and six narrative qualities. Users can quickly ﬁnd

speciﬁc types of data videos with the help of this classiﬁcation.

These studies provide a solid foundation for designers to create

data videos. Xu et al. [82] considered data videos’ opening

narrative and visual presentation design. They proposed six

cinematic opening styles (symbolism and metaphor, camera eye,

big bang, old footage, and ending ﬁrst styles) and 28 design

guidelines for the six styles.

Visual narratives in data videos are usually performed using

animation because animation can represent temporal changes

and enhance the comprehension and user engagement of data

stories [83]. Shi et al. [88] analyzed 43 animation techniques for

narrative visualizations and categorized eight narrative strategies

(e.g., emphasis, suspense, and comparison) to construct a design

space.This design space describes data video production and in-

tegration with visual narrative strategies, providing useful design

suggestions and weakening barriers to expressive data video

creation. By examining animated data charts, Tang et al. [89]

created a design space for data videos with ﬁve dimensions: data,

motion, layout, duration, and narrative. Moreover, they proposed

20 design guidelines based on these dimensions. In addition,

other researchers have conducted studies on how to i ncrease the

effectiveness of data video communication. Sallam et al. [84]

found that for a problem with no clear solution, a better option

is to present it in a data video because the audience may feel

high levels of negative emotions. To improve the quality and

reduce the complexity of data video, Wang et al. [85] proposed

4442 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

Fig. 8. Selected examples of data video’s design spaces and tools. (1) Design space: Amini et al. [80] states that E+I+PR+ in data video is the most balanced

narrative structure. (2) Authoring tool: example of a data-driven video generated using DataClip for ﬁnancial analysis [90]. (3) ML/AI-supported tool: Data

Animator’s storyboard editing work window. It is able to segment complicated animations by stacking keyframes and using data parameters to stagger the start

time and modify the pace of animated objects in the timeline view [93]. (4) ML/AI-generator tool: Autoclips automatically generates keyframes for data video

based o n a series of data facts [97].

nonlinear time mapping and foreshadowing. The “foreshadow-

ing” technique, as described by researchers, is only applicable

to animated stacked images. Li et al. [86] expanded on this

by formally deﬁning visual foreshadowing, a technique that

addresses the problem of time-consuming videos that ignore

the viewer’s attention. Shu et al. [87] examined the Data-GIFs

design space and offered recommendations.

Authoring Tool: Producing data videos is time-consuming

because it requires collaboration between people from different

backgrounds (e.g., data analysts to generate data and insights,

scripters to write narratives, and motion designers and graphics

experts to produce video assets). Each element may depend on

one or more particular software tools [90]. DataClips [90] pro-

vides a set of data clip libraries that allow amateurs to combine

data-driven clips to form longer sequences of data videos. Lan

et al. [91] developed Kineticharts, a chart animation scheme

for conveying emotions, based on the animations provided in

DataClips [90]. Compared with DataClips, Kineticharts [91]

can enhance the emotional engagement of users by improving

the presentation of the story without hindering users’ under-

standing of the data. In addition, Chen et al. [92] developed

VisCommentator, a tool for analyzing ball sports videos in sports

programs, facilitating the creation of enhanced sports analysis

videos through data insights and visualization suggestions.

ML/AI-Supported Tool: Researchers have designed and de-

veloped tools to transform static visualizations into dynamic

versions. Data Animator [93] utilizes the Data Illustrator [136]

framework to match two static visual objects and generate

automatic transitions by default. The tool also supports divid-

ing complex animations into segments by layering keyframes,

utilizing data characteristics to stagger the start time, and ad-

justing the pace of animated objects through a timeline view.

Similarly, InfoMotion [94] can build data ﬁlms by extracting

the graphical attributes of infographics, understanding its un-

derlying information structure, and adding animation effects to

the visual pieces of the infographic in chronological sequence.

As InfoMotion [94] is built into PowerPoint [128] as a plug-in,

it can automatically link a variety of built-in animation effects

to the visual parts of slides, which is excellent for speeding up

data video production. This tool [94] is also easier to create data

videos than Data Animator [93] because it can only use data in

Data Illustrator [136] format. In addition, while Gemini2 [95]

and Cast [96] are not dedicated tools for creating data videos,

both tools can build keyframes for charts. Gemini2 [95] focuses

on helping users create animations by referring to keyframe sug-

gestions. Similarly, Cast [96] allows users to manipulate directly

to change the parameters of animation effects (e.g., animation

type and jogging function) and reﬁne animation speciﬁcations

(e.g., adjusting keyframes to play across tracks and adjusting

delays) by providing a GUI interface. ML/AI-supported tools

for data video creation identify existing infographic elements

and convert them into dynamic video clips, while authoring tools

provide a library of data clips for direct use.

ML/AI-Generator Tool: While the abovementioned technolo-

gies ease the design process, data videos are still difﬁcult

to create because users must select which visualizations and

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4443

animations to utilize and how to assemble a cohesive video.

The aforementioned problem was solved by the emergence of

AutoClips [97] is a tool that automatically creates a data video

from a series of facts, saving users time and reducing complexity

by eliminating the need for data analysis and animation creation

using video motion software. However, it has limitations, as it

only supports tabular data and tends to favor datasets with diverse

column types.

Summary: Data video has become popular owing to the

growth of social media platforms. Research on data video has

also received much attention in recent years. At the design space

level, researchers have explored the understanding, creation,

and dissemination of data videos to help better understand the

components of data videos. These design spaces and guidelines

provide the theoretical basis for developing authoring tools,

ML/AI-supported tools, and ML/AI-generator tools. Authoring

tools simplify the creation of data videos by offering a library

of existing data clips that can be replicated. ML/AI-supported

tools focus on how to identify existing s tatic visualization ele-

ments and convert them into dynamic videos. ML/AI-generator

tools can automatically generate data videos directly from

input data.

However, certain issues still need further investigation. First,

existing automatic tools for creating data videos are still limited

to a few visualization genres and input formats. For example,

AutoClips [97] only supports tabular data, limiting the visual

display possibilities of data video. More tools are needed to

handle various data types, such as spatial-temporal data and

textual data, which are essential for constructing diverse data

narratives. Second, researchers also a need to study how the

speed, continuity, and smoothness of animations in data videos,

the transitions between charts and graphs, and the embellishment

effects added to the videos would affect the understanding and

overall experience of readers [91].

IX. D

ISCUSSIONS AND FUTURE WORK

In this section, we outline the current limitations and future

research opportunities of design spaces and tools at different

automation levels for narrative visualization.

Design space aims to describe all the possible design

aspects for various narrative genres. The summary of

the design space allows us to capture some implicit

knowledge of visual designers and practitioners [104]. Most ex-

isting studies propose clear design guidelines in speciﬁc design

scenarios [60], [89], [137]. However, the design space articles

on the different narrative genres vary in focus. For annotated

charts and infographic genres, the focus is on how to effectively

create a correct and aesthetically appealing visualization. For

timeline, data comics, scrollytelling, and data video genres, the

focus is more on exploring the narrative structure. In particular,

data video pays special attention to creating animations, while

other genres focus more on static presentations. Below are the

major future research directions.

Simplify and Validate the Design Space. Creators can gen-

erally access many existing visualization design guidelines,

but choosing the right guidelines is difﬁcult for them. More-

over, design guidelines often fall short in explaining when it

is more appropriate to use, and lack proper validation [89].

For example, researchers have proposed visualization design

process frameworks [138], [139], but have not explained what

scenarios and how to use these frameworks for visualization

design. Therefore, a potential research direction is to validate

the usage of various design spaces and classify them according

to application domains. Amateurs may also be provided with an

overview of design spaces to tackle speciﬁc design problems.

For more experienced designers, we could pay more attention to

the subtle design guidelines that can improve the user experience

and user perception in the visualization.

Explore New Narrative Structures. The existing narrative

structures are primarily derived from movies or other audio-

visual content [80]. Information on the application of narrative

structures in novels and plays in narrative visualizations is

generally lacking in the extant literature. Due to the different

characteristics of various narrative genres, the choice of narrative

structure can also be different. For example, a timeline mainly

presents content in a linear narrative sequence [50], whereas in

data videos, using a nonlinear narrative approach is more likely

to engage the audience [43]. Therefore, a potential research

direction is to explore different data types and which narrative

structure is more suitable for different narrative genres.

Explore other Narrative Visualization Genres. Some tradi-

tional visualization genres that focus more on visual analytics

are embracing narrative and storytelling concepts. Suprata [137]

noted that adding narrative attributes to dashboards allows users

to become more aware of their goals and how to take action

next. Fernandez Nieto et al. [140] enhanced teachers’ guidance

of the content by including narrative attributes in designing

learning analytics dashboards. With more attention and practical

applications of narrative attributes to traditional dashboards, nar-

rative dashboards can be another future narrative genre. Mean-

while, some new genres of visualizations have emerged, such as

immersive visualization [141] and data physicalization [142],

which can also be developed with narrative characteristics. The

potential research direction of narrative immersive visualization

is a more in-depth exploration of data types, spatial layouts,

and user interactions for narrative communication in the virtual

environment. The physicalization of data encodes information

in a perceptible form, allowing users to explore using all their

senses and motor skills [143]. More research on narrative data

physicalization is needed to understand the design space, data

production process, and beneﬁts compared to ﬂat visualization

or virtual presentation [144].

Authoring tools aims to facilitate the visualization cre-

ation process with controllable interactions. These tools

include stand-alone applications [10], [64], web-based

tools [28], [124], and authoring tools that combine with ofﬁce

software [29], [30]. These tools offer users enough control

to create customized visualizations, even complex ones that

cannot be supported by automated tools. Although these au-

thoring tools signiﬁcantly improve the efﬁciency of creating

narrative visualizations, most tools are aimed at users with

4444 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 30, NO. 8, AUGUST 2024

a certain level of expertise. For example, authoring tools for

infographics and timelines require users to have visual design

skills, while data videos require users to have video editing

skills. Future research may invest more efforts in the following

directions.

Develop Flexible Interfaces for Authoring Tools. Among

narrative authoring tools, a few tools can be used to freely

draw creative patterns on a screen, including DataSelﬁes [33],

DataInk [34] and SketchStory [35] for infographics and Data-

Toon [64] for data comics. However, other narrative visualiza-

tion tools are relatively lacking. Providing more ﬂexible inter-

face methods can help designers achieve more creative ideas and

more artistic effects in creating various narrative visualizations.

Develop More Interactive Visualizations. Among the existing

narrative visualization genres, only scrollytelling and slideshow

have strong interactive properties, while annotated charts,

infographics, data comics, and data videos are mostly static vi-

sualizations that lack interactive functionality. However, studies

have proven that by providing interactivity [66] and adding inter-

esting [26], [27] and emotional factors [25] to the visualization,

users are more likely to memorize the information. Therefore, in

future research, a possible approach is to explore more narrative

genres of interactive visualizations and to add interesting and

emotional elements.

ML/AI-supported tools are designed to assist users in

visualization creation by applying intelligent algorithms

and techniques. Such tools can provide recommenda-

tions or guide the user via the creation process. ML/AI-supported

tools for narrative visualizations can serve a wider range of

users than authoring tools. For example, designers who lack

data analytic skills can easily create data comics with the data

analysis capabilities of ML/AI-supported tools; data analysts

who lack design skills can use ML/AI-supported tools to create

more aesthetically pleasing timelines or data videos.

However, the automatic goals and functions of current

ML/AI-supported tools for different narrative visualization

types are different. For example, tools for annotated charts,

infographics, and data comics have the auxiliary function of

identifying and parsing visualizations. Among them, the purpose

of annotated chart recognition is to add annotations to facilitate

comprehension of the visual story; the purpose of infographic

recognition is to create new visualizations based on the orig-

inal visual styles; and the purpose of data comics recognition

is to transform visual content into the comic layout. ML/AI-

supported tools for timelines focus on placing timeline text and

optimizing visual aesthetic effects, while data videos pay more

attention to the creation of animation. By summarizing existing

research in ML/AI-supported tools for narrative visualization,

the following directions can be studied.

Additionally, most existing tools’ annotations only explain

statistical information on a single chart [32], and there is a lack

of studies that apply intelligent techniques to extract contextual

information for building visualizations with narrative structures.

Improve the Reusability of Existing Visualizations. In practice,

the majority of charts are saved as bitmap pictures. Although

they are simple to spread and use, they are difﬁcult to modify.

VisCode [145] and Chartem [146] can store and hide the original

data information inside the picture of a chart. However, only

rudimentary visual charts are supported by these tools. There-

fore, tools to support the recognition and reprocessing of more

complex visual charts and more diverse narrative genres must

be developed. By improving the reusability of existing narrative

visualizations, amateurs are able to create more visual stories

efﬁciently and effectively [93].

Facilitate the Adaptability of Different Software. Some exist-

ing tools are integrated with ofﬁce software. For example, all

the features of the DataComicsJS [56] tool can be replicated

in presentation tools (e.g., Microsoft PowerPoint [128]) and

drawing tools (e.g., Adobe Illustrator [117]). Chartreuse [29]

and InfoNice [30] are also both integrated into Microsoft Ofﬁce

software in the form of plug-ins. After incorporating the natural

language algorithms of intelligent tools into productivity soft-

ware, the corresponding functions can work in the background.

For example, when a statement can be enhanced with visualiza-

tion, a message can ask if the user wants to use a recommended

chart [39]. This way, ML/AI-supported tools can reach a wider

audience.

ML/AI-generator tools are more intelligent than the pre-

vious three types of tools in that they require minimal

or no user involvement in the entire creation process.

These tools automate the analysis of data and directly generate

a complete narrative visualization without user intervention.

ML/AI-generator tools mostly target amateurs. The develop-

ment of such tools has gradually increased in the past decade.

As visual communication becomes increasingly important in our

daily life, we believe that such tools can play an important role in

the creation of narrative visualization. The following directions

can be studied in terms of understanding user intent to improve

accuracy and efﬁciency.

Improve data analysis capability to identify user design in-

tent. Among the current six genres of narrative visualizations,

ML/AI-generator tools that can be used for timelines are gener-

ally lacking. Even though certain intelligent tools can be used

to create a timeline, they only modify the local area. Complet-

ing the entire creation remains time-consuming. While several

ML/AI-generator tools for other genres, such as Autoclips [97],

can analyze the data and extract essential parameters from t he

dataset, the ﬁnal output is not satisfactory when facing different

datasets, different contexts, or more complex data types. There-

fore, the ability of ML/AI-generator tools to analyze complex

data in the future must be improved. In addition, a possible

direction is to study how to input the user’s creative intent into the

automation process and at which point in the creation process;

in this manner, the user’s intent can be fully grasped to achieve

the most satisfying outcome.

Develop narrative recommendation tools to clarify design

intent. In statistical charts, researchers have developed many

visual recommendation systems such as Voyager [147] and

SeeDB [148]. However, research on such tools, speciﬁcally

for narrative visualization, is lacking. This situation can be

explained by recommendation methods being based on data

characteristics or design guidelines rather than the user’s de-

sign intent. Creating a narrative visualization recommendation

platform to store both design processes and outcomes could be a

CHEN et al.: HOW DOES AUTOMATION SHAPE THE PROCESS OF NARRATIVE VISUALIZATION: A SURVEY OF TOOLS 4445

potential research direction. By analyzing the collected informa-

tion, we could identify the user’s design intent with the help of

machine learning algorithms [149]. Such recommendation tools

can provide users with abundant design ideas and recommenda-

tions in the pre-creation stage of narrative visualizations.

This study outlines four narrative visualization tools at the

automation level. Furthermore, although the different tools are

divided into different narrative visualization genres in this study,

it does not mean that these tools can only create one genre of

narrative visualization. The tools present certain compatibility

across different genres of narrative visualizations. However,

novice users need to undergo a learning curve to varying degrees

for either visualization tool type. Moreover, these visualization

tools can only tell users how the data have changed, without

explaining why it has changed, suggesting that the user will still

need to analyze the reasons for the data change results. More-

over, a one-size-ﬁts-all tool to handle all scenarios to address

different users and goals does not exist. Therefore, all four levels

of automation have their unique values and are worth further in-

vestigation, from pure manual design following design theories

to the ultimate ML/AI-generated tools that support automation

in the whole visualization creation pipeline. Furthermore, with

the development of AI technology and the need to create and

share data visualization by amateurs, ML/AI-supported tools

and ML/AI-generated tools are becoming more popular in both

research and various application domains. ML/AI-supported

tools, with human participation and machine assistance, offer a

superior user experience and more diverse design opportunities

compared to authoring and generator tools. More efforts can

be invested in such human-centered ML/AI-supported narrative

visualization tools in the future.

X. C

ONCLUSION

In this study, we systematically reviewed 105 papers and tools

to study how automation can progressively engage in visualiza-

tion design and narrative processes to help users create narrative

visualizations more easily, effectively, and efﬁciently. We have

summarized six genres of narrative visualization (i.e., anno-

tated charts, infographics, timeline & storyline, data comics,

scrollytelling & slideshow, and data videos) based on previous

research, and four types of tools (i.e., design space, authoring

tool, ML/AI-supported tool, ML/AI-generator tool) based on

the intelligence and automation level of the tools. This study

enables users to comprehend the explicit and implicit design el-

ements of various narrative visualization genres, facilitating the

selection of appropriate tools for visual storytelling. However,

our survey excluded scientiﬁc visualization. In the ﬁeld of sci-

entiﬁc visualization, narrative visualization has been applied in

scenarios such as climate or medical condition narratives [150].

We believe that more research and tools in scientiﬁc visualization

storytelling can be performed and developed in the future. We

further discuss new research challenges and outline potential

directions for future research and implementation.

CKNOWLEDGMENTS

We would like to thank anonymous reviewers for their con-

structive feedback.

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Qing Chen received the BEng degree from the De-

partment of Computer Science, Zhejiang University,

and the PhD degree from the Department of Computer

Science and Engineering, Hong Kong University of

Science and Technology (HKUST). After receiving

the PhD degree, she worked as a postdoc with Inria

and Ecole Polytechnique. She is currently an assistant

professor with Tongji University. Her research inter-

ests include information visualization, visual analyt-

ics, human-computer interaction, online education,

visual storytelling, intelligent healthcare and design.

Shixiong Cao received the master’s degree in design

from Sangmyung University, South Korea, in 2019,

and the PhD degree from Sungkyunkwan University,

South Korea, in 2023. Currently, he works as a post-

doctoral researcher with Tongji University, and his

research interests include information design, narra-

tive visualization design, and user experience design.

Jiazhe Wang received the master’s degree from the

Department of Computer Science, University of Ox-

ford. He is currently a data and front-end technologist

with Ant Group, a core member of the data visualiza-

tion team AntV. He is also a tech leader of the aug-

mented analytics team for the internal BI product of

Ant Group. His research interests include automated

visualization, augmented analytics and narrative vi-

sualization.

Nan Cao received the PhD degree in computer sci-

ence and engineering from the Hong Kong University

of Science and Technology (HKUST), Hong Kong,

China, in 2012. He is currently a professor with

Tongji University and the assistant dean of the Tongji

College of Design and Innovation. He also directs

the Tongji Intelligent Big Data Visualization Lab

(iDV

Lab) and conducts interdisciplinary research

across multiple ﬁelds, including data visualization,

human computer interaction, machine learning, and

data mining. He was a research staff member with

the IBM T.J. Watson Research Center, New York, NY, USA before joining the

Tongji faculty, in 2016.